Overview

Dataset statistics

Number of variables32
Number of observations450000
Missing cells2004784
Missing cells (%)13.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory109.9 MiB
Average record size in memory256.0 B

Variable types

DateTime7
Numeric18
Categorical7

Alerts

first_mile_distance is highly correlated with total_distHigh correlation
last_mile_distance is highly correlated with total_distHigh correlation
alloted_orders is highly correlated with delivered_orders and 2 other fieldsHigh correlation
delivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
undelivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
lifetime_order_count is highly correlated with alloted_orders and 1 other fieldsHigh correlation
total_dist is highly correlated with first_mile_distance and 1 other fieldsHigh correlation
order_time_hour is highly correlated with accept_time_hourHigh correlation
accept_time_hour is highly correlated with order_time_hourHigh correlation
delivered_fraction is highly correlated with undelivered_ordersHigh correlation
first_mile_distance is highly correlated with total_distHigh correlation
last_mile_distance is highly correlated with total_distHigh correlation
alloted_orders is highly correlated with delivered_ordersHigh correlation
delivered_orders is highly correlated with alloted_ordersHigh correlation
reassigned_order is highly correlated with accept_order_diffHigh correlation
total_dist is highly correlated with first_mile_distance and 1 other fieldsHigh correlation
order_time_hour is highly correlated with accept_time_hourHigh correlation
accept_time_hour is highly correlated with order_time_hourHigh correlation
accept_order_diff is highly correlated with reassigned_orderHigh correlation
last_mile_distance is highly correlated with total_distHigh correlation
alloted_orders is highly correlated with delivered_ordersHigh correlation
delivered_orders is highly correlated with alloted_ordersHigh correlation
undelivered_orders is highly correlated with delivered_fractionHigh correlation
total_dist is highly correlated with last_mile_distanceHigh correlation
order_time_hour is highly correlated with accept_time_hourHigh correlation
accept_time_hour is highly correlated with order_time_hourHigh correlation
delivered_fraction is highly correlated with undelivered_ordersHigh correlation
reassignment_method is highly correlated with reassigned_orderHigh correlation
reassigned_order is highly correlated with reassignment_method and 1 other fieldsHigh correlation
reassignment_reason is highly correlated with reassigned_orderHigh correlation
order_id is highly correlated with order_date and 2 other fieldsHigh correlation
order_date is highly correlated with order_id and 2 other fieldsHigh correlation
first_mile_distance is highly correlated with total_distHigh correlation
last_mile_distance is highly correlated with total_distHigh correlation
alloted_orders is highly correlated with delivered_orders and 1 other fieldsHigh correlation
delivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
undelivered_orders is highly correlated with alloted_orders and 1 other fieldsHigh correlation
session_time is highly correlated with order_time_hour and 1 other fieldsHigh correlation
total_dist is highly correlated with first_mile_distance and 1 other fieldsHigh correlation
weekday is highly correlated with order_id and 2 other fieldsHigh correlation
order_time_hour is highly correlated with session_time and 1 other fieldsHigh correlation
accept_time_hour is highly correlated with session_time and 1 other fieldsHigh correlation
weekday_num is highly correlated with order_id and 2 other fieldsHigh correlation
delivered_time has 5218 (1.2%) missing values Missing
reassignment_method has 436256 (96.9%) missing values Missing
reassignment_reason has 436247 (96.9%) missing values Missing
cancelled_time has 444782 (98.8%) missing values Missing
delivered_fraction has 16948 (3.8%) missing values Missing
call_total has 159543 (35.5%) missing values Missing
call_support has 431560 (95.9%) missing values Missing
call_customer has 71338 (15.9%) missing values Missing
alloted_orders has 16948 (3.8%) zeros Zeros
delivered_orders has 17341 (3.9%) zeros Zeros
undelivered_orders has 250027 (55.6%) zeros Zeros
weekday_num has 37833 (8.4%) zeros Zeros

Reproduction

Analysis started2022-02-16 11:21:37.807957
Analysis finished2022-02-16 11:23:24.928178
Duration1 minute and 47.12 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Distinct252868
Distinct (%)56.2%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum2021-01-26 02:21:35
Maximum2021-02-06 10:03:24
2022-02-16T16:53:25.000673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:25.114552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_id
Real number (ℝ≥0)

HIGH CORRELATION

Distinct449999
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean369143.0808
Minimum118350
Maximum594842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:25.229641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum118350
5-th percentile167343.95
Q1257342.75
median369842.5
Q3482342.25
95-th percentile572342.05
Maximum594842
Range476492
Interquartile range (IQR)224999.5

Descriptive statistics

Standard deviation131146.9064
Coefficient of variation (CV)0.355273912
Kurtosis-1.149146297
Mean369143.0808
Median Absolute Deviation (MAD)112500
Skewness-0.03426737717
Sum1.661143863 × 1011
Variance1.719951106 × 1010
MonotonicityNot monotonic
2022-02-16T16:53:25.339795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1814022
 
< 0.1%
5567531
 
< 0.1%
3064221
 
< 0.1%
3064331
 
< 0.1%
3064301
 
< 0.1%
3064291
 
< 0.1%
3064281
 
< 0.1%
3064311
 
< 0.1%
3064321
 
< 0.1%
3064271
 
< 0.1%
Other values (449989)449989
> 99.9%
ValueCountFrequency (%)
1183501
< 0.1%
1183511
< 0.1%
1183521
< 0.1%
1183531
< 0.1%
1183541
< 0.1%
1183551
< 0.1%
1183561
< 0.1%
1183571
< 0.1%
1183581
< 0.1%
1183591
< 0.1%
ValueCountFrequency (%)
5948421
< 0.1%
5948411
< 0.1%
5948401
< 0.1%
5948391
< 0.1%
5948381
< 0.1%
5948371
< 0.1%
5948361
< 0.1%
5948351
< 0.1%
5948341
< 0.1%
5948331
< 0.1%

order_date
Date

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum2021-01-26 00:00:00
Maximum2021-02-06 00:00:00
2022-02-16T16:53:25.437794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:25.519926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct246871
Distinct (%)54.9%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Minimum2021-01-26 02:21:59
Maximum2021-02-06 10:52:57
2022-02-16T16:53:25.615519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:25.724804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct254201
Distinct (%)56.5%
Missing157
Missing (%)< 0.1%
Memory size3.4 MiB
Minimum2021-01-26 02:22:08
Maximum2021-02-06 10:53:03
2022-02-16T16:53:25.836651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:25.946434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct257117
Distinct (%)57.4%
Missing2421
Missing (%)0.5%
Memory size3.4 MiB
Minimum2021-01-26 02:32:51
Maximum2021-02-08 07:52:34
2022-02-16T16:53:26.055166image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:26.163253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

delivered_time
Date

MISSING

Distinct257067
Distinct (%)57.8%
Missing5218
Missing (%)1.2%
Memory size3.4 MiB
Minimum2021-01-26 02:49:47
Maximum2021-02-08 16:19:04
2022-02-16T16:53:26.277840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:26.412998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

rider_id
Real number (ℝ≥0)

Distinct19537
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7763.244016
Minimum0
Maximum21566
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:26.561780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile519
Q12805
median6754
Q311965
95-th percentile18050
Maximum21566
Range21566
Interquartile range (IQR)9160

Descriptive statistics

Standard deviation5592.880135
Coefficient of variation (CV)0.7204308049
Kurtosis-0.8427443266
Mean7763.244016
Median Absolute Deviation (MAD)4436
Skewness0.4790871039
Sum3493459807
Variance31280308.2
MonotonicityNot monotonic
2022-02-16T16:53:26.664462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
237228
 
0.1%
190213
 
< 0.1%
11998209
 
< 0.1%
170203
 
< 0.1%
853200
 
< 0.1%
747199
 
< 0.1%
2280195
 
< 0.1%
4921194
 
< 0.1%
12709191
 
< 0.1%
453190
 
< 0.1%
Other values (19527)447978
99.6%
ValueCountFrequency (%)
07
 
< 0.1%
116
 
< 0.1%
228
 
< 0.1%
43
 
< 0.1%
519
 
< 0.1%
615
 
< 0.1%
929
 
< 0.1%
1097
< 0.1%
1125
 
< 0.1%
1224
 
< 0.1%
ValueCountFrequency (%)
215661
< 0.1%
215651
< 0.1%
215641
< 0.1%
215631
< 0.1%
215621
< 0.1%
215611
< 0.1%
215602
< 0.1%
215592
< 0.1%
215582
< 0.1%
215571
< 0.1%

first_mile_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct93743
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.229888977
Minimum0.0001342587859
Maximum42.0381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:26.776239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0001342587859
5-th percentile0.05458151785
Q10.539575
median1.1387
Q31.853
95-th percentile2.7079
Maximum42.0381
Range42.03796574
Interquartile range (IQR)1.313425

Descriptive statistics

Standard deviation0.8461826096
Coefficient of variation (CV)0.688015443
Kurtosis12.36678626
Mean1.229888977
Median Absolute Deviation (MAD)0.6482
Skewness0.7588916893
Sum553450.0398
Variance0.7160250088
MonotonicityNot monotonic
2022-02-16T16:53:26.878356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.715339
 
< 0.1%
0.740439
 
< 0.1%
0.696536
 
< 0.1%
0.695936
 
< 0.1%
0.724134
 
< 0.1%
0.657834
 
< 0.1%
0.688334
 
< 0.1%
1.000934
 
< 0.1%
1.535634
 
< 0.1%
0.617434
 
< 0.1%
Other values (93733)449646
99.9%
ValueCountFrequency (%)
0.00013425878592
 
< 0.1%
0.00021228177971
 
< 0.1%
0.00028480589391
 
< 0.1%
0.0003288655191
 
< 0.1%
0.00037974119192
 
< 0.1%
0.00040277635785
< 0.1%
0.000413813371
 
< 0.1%
0.00044528601772
 
< 0.1%
0.00045529369473
< 0.1%
0.00046508607722
 
< 0.1%
ValueCountFrequency (%)
42.03811
< 0.1%
17.34421
< 0.1%
11.67411
< 0.1%
11.36491
< 0.1%
10.75251
< 0.1%
10.69941
< 0.1%
10.58391
< 0.1%
10.05461
< 0.1%
9.66951
< 0.1%
9.60791
< 0.1%

last_mile_distance
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1331
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.968872711
Minimum0
Maximum22.41
Zeros94
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:26.986414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q11.47
median2.67
Q34.22
95-th percentile6.22
Maximum22.41
Range22.41
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation1.884123753
Coefficient of variation (CV)0.6346259797
Kurtosis0.8795462088
Mean2.968872711
Median Absolute Deviation (MAD)1.33
Skewness0.8267706027
Sum1335992.72
Variance3.549922316
MonotonicityNot monotonic
2022-02-16T16:53:27.086405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.581143
 
0.3%
1.51121
 
0.2%
1.971119
 
0.2%
1.451111
 
0.2%
1.631110
 
0.2%
1.461109
 
0.2%
1.541107
 
0.2%
1.321105
 
0.2%
1.761104
 
0.2%
1.671104
 
0.2%
Other values (1321)438867
97.5%
ValueCountFrequency (%)
094
 
< 0.1%
0.01170
 
< 0.1%
0.02210
< 0.1%
0.03193
< 0.1%
0.04237
0.1%
0.05253
0.1%
0.06291
0.1%
0.07353
0.1%
0.08415
0.1%
0.09482
0.1%
ValueCountFrequency (%)
22.411
< 0.1%
22.261
< 0.1%
21.271
< 0.1%
21.211
< 0.1%
21.21
< 0.1%
21.121
< 0.1%
19.811
< 0.1%
18.131
< 0.1%
17.271
< 0.1%
16.851
< 0.1%

alloted_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct511
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.6806533
Minimum0
Maximum567
Zeros16948
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:27.197227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q131
median77
Q3144
95-th percentile286
Maximum567
Range567
Interquartile range (IQR)113

Descriptive statistics

Standard deviation90.63736221
Coefficient of variation (CV)0.9002460673
Kurtosis2.094703755
Mean100.6806533
Median Absolute Deviation (MAD)53
Skewness1.382605843
Sum45306294
Variance8215.131429
MonotonicityNot monotonic
2022-02-16T16:53:27.833673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016948
 
3.8%
63764
 
0.8%
53717
 
0.8%
43648
 
0.8%
23589
 
0.8%
73586
 
0.8%
33513
 
0.8%
103424
 
0.8%
83309
 
0.7%
93168
 
0.7%
Other values (501)401334
89.2%
ValueCountFrequency (%)
016948
3.8%
12991
 
0.7%
23589
 
0.8%
33513
 
0.8%
43648
 
0.8%
53717
 
0.8%
63764
 
0.8%
73586
 
0.8%
83309
 
0.7%
93168
 
0.7%
ValueCountFrequency (%)
56712
< 0.1%
56518
< 0.1%
56317
< 0.1%
56118
< 0.1%
5589
 
< 0.1%
55525
< 0.1%
55321
< 0.1%
54528
< 0.1%
54021
< 0.1%
53620
< 0.1%

delivered_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct505
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.94466
Minimum0
Maximum562
Zeros17341
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:27.937286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q131
median77
Q3143
95-th percentile284
Maximum562
Range562
Interquartile range (IQR)112

Descriptive statistics

Standard deviation90.14421802
Coefficient of variation (CV)0.9019413145
Kurtosis2.113953265
Mean99.94466
Median Absolute Deviation (MAD)52
Skewness1.387814359
Sum44975097
Variance8125.980042
MonotonicityNot monotonic
2022-02-16T16:53:28.032816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
017341
 
3.9%
63798
 
0.8%
43698
 
0.8%
53697
 
0.8%
73645
 
0.8%
33528
 
0.8%
23525
 
0.8%
103403
 
0.8%
83322
 
0.7%
133228
 
0.7%
Other values (495)400815
89.1%
ValueCountFrequency (%)
017341
3.9%
12949
 
0.7%
23525
 
0.8%
33528
 
0.8%
43698
 
0.8%
53697
 
0.8%
63798
 
0.8%
73645
 
0.8%
83322
 
0.7%
93180
 
0.7%
ValueCountFrequency (%)
56212
< 0.1%
56018
< 0.1%
55917
< 0.1%
55718
< 0.1%
5539
 
< 0.1%
55025
< 0.1%
54821
< 0.1%
54028
< 0.1%
53521
< 0.1%
53120
< 0.1%

cancelled
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
444782 
1
 
5218

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0444782
98.8%
15218
 
1.2%

Length

2022-02-16T16:53:28.127794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:28.183693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0444782
98.8%
15218
 
1.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

undelivered_orders
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7347177778
Minimum0
Maximum9
Zeros250027
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:28.238210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.056017171
Coefficient of variation (CV)1.437309948
Kurtosis4.89193206
Mean0.7347177778
Median Absolute Deviation (MAD)0
Skewness1.89921989
Sum330623
Variance1.115172265
MonotonicityNot monotonic
2022-02-16T16:53:28.306395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0250027
55.6%
1118103
26.2%
250582
 
11.2%
320011
 
4.4%
47733
 
1.7%
51851
 
0.4%
6932
 
0.2%
7600
 
0.1%
8105
 
< 0.1%
956
 
< 0.1%
ValueCountFrequency (%)
0250027
55.6%
1118103
26.2%
250582
 
11.2%
320011
 
4.4%
47733
 
1.7%
51851
 
0.4%
6932
 
0.2%
7600
 
0.1%
8105
 
< 0.1%
956
 
< 0.1%
ValueCountFrequency (%)
956
 
< 0.1%
8105
 
< 0.1%
7600
 
0.1%
6932
 
0.2%
51851
 
0.4%
47733
 
1.7%
320011
 
4.4%
250582
 
11.2%
1118103
26.2%
0250027
55.6%

lifetime_order_count
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2887
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean853.5401244
Minimum0
Maximum30469
Zeros58
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:28.398467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1165
median396
Q3948
95-th percentile3013
Maximum30469
Range30469
Interquartile range (IQR)783

Descriptive statistics

Standard deviation1502.9162
Coefficient of variation (CV)1.760803221
Kurtosis77.97124105
Mean853.5401244
Median Absolute Deviation (MAD)287
Skewness6.75716413
Sum384093056
Variance2258757.105
MonotonicityNot monotonic
2022-02-16T16:53:28.500570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
501609
 
0.4%
511230
 
0.3%
2901201
 
0.3%
1301170
 
0.3%
1661162
 
0.3%
2281161
 
0.3%
991104
 
0.2%
841091
 
0.2%
571086
 
0.2%
1061084
 
0.2%
Other values (2877)438102
97.4%
ValueCountFrequency (%)
058
 
< 0.1%
1172
< 0.1%
2232
0.1%
3293
0.1%
4280
0.1%
5381
0.1%
6344
0.1%
7428
0.1%
8369
0.1%
9375
0.1%
ValueCountFrequency (%)
3046950
< 0.1%
2797255
< 0.1%
2681026
 
< 0.1%
249334
 
< 0.1%
2402280
< 0.1%
2362633
< 0.1%
2258134
< 0.1%
2203327
 
< 0.1%
2199510
 
< 0.1%
219387
 
< 0.1%

reassignment_method
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing436256
Missing (%)96.9%
Memory size3.4 MiB
auto
13383 
manual
 
361

Length

Max length6
Median length4
Mean length4.052532014
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowauto
2nd rowauto
3rd rowauto
4th rowauto
5th rowauto

Common Values

ValueCountFrequency (%)
auto13383
 
3.0%
manual361
 
0.1%
(Missing)436256
96.9%

Length

2022-02-16T16:53:28.616431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:28.685101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
auto13383
97.4%
manual361
 
2.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reassignment_reason
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)< 0.1%
Missing436247
Missing (%)96.9%
Memory size3.4 MiB
Auto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
7212 
Reassignment Request from SE portal.
5300 
Reassign
1241 

Length

Max length76
Median length76
Mean length54.44921108
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReassignment Request from SE portal.
2nd rowReassignment Request from SE portal.
3rd rowAuto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
4th rowAuto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket
5th rowReassignment Request from SE portal.

Common Values

ValueCountFrequency (%)
Auto Reassignment basis Inaction. coreengine.tasks.repush_order_to_aa_bucket7212
 
1.6%
Reassignment Request from SE portal.5300
 
1.2%
Reassign1241
 
0.3%
(Missing)436247
96.9%

Length

2022-02-16T16:53:28.748140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:28.812184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
reassignment12512
19.6%
auto7212
11.3%
basis7212
11.3%
inaction7212
11.3%
coreengine.tasks.repush_order_to_aa_bucket7212
11.3%
request5300
8.3%
from5300
8.3%
se5300
8.3%
portal5300
8.3%
reassign1241
 
1.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reassigned_order
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0.0
436247 
1.0
 
13753

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0436247
96.9%
1.013753
 
3.1%

Length

2022-02-16T16:53:28.883657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:28.935163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0436247
96.9%
1.013753
 
3.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

session_time
Real number (ℝ≥0)

HIGH CORRELATION

Distinct65872
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.1078929
Minimum0
Maximum1298.966667
Zeros16
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:29.001171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.949166667
Q184.85
median175.55
Q3314.6875
95-th percentile580.5008333
Maximum1298.966667
Range1298.966667
Interquartile range (IQR)229.8375

Descriptive statistics

Standard deviation176.0372292
Coefficient of variation (CV)0.7997769952
Kurtosis0.3322033297
Mean220.1078929
Median Absolute Deviation (MAD)103.2333333
Skewness0.9955830323
Sum99048551.78
Variance30989.10605
MonotonicityNot monotonic
2022-02-16T16:53:29.110680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
175.553697
 
0.8%
0.06666666667155
 
< 0.1%
0.03333333333144
 
< 0.1%
0.1166666667136
 
< 0.1%
0.15132
 
< 0.1%
0.05129
 
< 0.1%
0.08333333333124
 
< 0.1%
240120
 
< 0.1%
0.1118
 
< 0.1%
0.1333333333117
 
< 0.1%
Other values (65862)445128
98.9%
ValueCountFrequency (%)
016
 
< 0.1%
0.01666666667115
< 0.1%
0.03333333333144
< 0.1%
0.05129
< 0.1%
0.06666666667155
< 0.1%
0.08333333333124
< 0.1%
0.1118
< 0.1%
0.1166666667136
< 0.1%
0.1333333333117
< 0.1%
0.15132
< 0.1%
ValueCountFrequency (%)
1298.9666671
< 0.1%
1294.4333331
< 0.1%
1250.3333331
< 0.1%
1183.5666671
< 0.1%
1141.5333331
< 0.1%
1096.8333331
< 0.1%
1076.851
< 0.1%
1057.4833331
< 0.1%
1043.8666671
< 0.1%
1038.2166671
< 0.1%

cancelled_time
Date

MISSING

Distinct5176
Distinct (%)99.2%
Missing444782
Missing (%)98.8%
Memory size3.4 MiB
Minimum2021-01-26 04:08:50
Maximum2021-02-07 10:04:25
2022-02-16T16:53:29.221643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:29.332125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

total_dist
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct187961
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.198761688
Minimum0.003223609088
Maximum45.8281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:29.441717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.003223609088
5-th percentile1.143691697
Q12.5178
median3.9779
Q35.6016
95-th percentile7.9983
Maximum45.8281
Range45.82487639
Interquartile range (IQR)3.0838

Descriptive statistics

Standard deviation2.163998339
Coefficient of variation (CV)0.5153896552
Kurtosis0.8577599078
Mean4.198761688
Median Absolute Deviation (MAD)1.531
Skewness0.669952122
Sum1889442.76
Variance4.68288881
MonotonicityNot monotonic
2022-02-16T16:53:29.544291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.297220
 
< 0.1%
4.779718
 
< 0.1%
3.643317
 
< 0.1%
3.819716
 
< 0.1%
3.125316
 
< 0.1%
3.188616
 
< 0.1%
3.709216
 
< 0.1%
4.085816
 
< 0.1%
3.209616
 
< 0.1%
4.252215
 
< 0.1%
Other values (187951)449834
> 99.9%
ValueCountFrequency (%)
0.0032236090881
< 0.1%
0.0089918312141
< 0.1%
0.01219585981
< 0.1%
0.017710459091
< 0.1%
0.017797419261
< 0.1%
0.02094609181
< 0.1%
0.023907616131
< 0.1%
0.025667316461
< 0.1%
0.025741299461
< 0.1%
0.027296731921
< 0.1%
ValueCountFrequency (%)
45.82811
< 0.1%
23.46941
< 0.1%
23.15141
< 0.1%
23.13531
< 0.1%
22.94681
< 0.1%
22.91311
< 0.1%
21.87061
< 0.1%
21.54421
< 0.1%
21.53251
< 0.1%
19.08251
< 0.1%

weekday
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
Friday
83548 
Thursday
80468 
Wednesday
80462 
Tuesday
78965 
Saturday
49470 
Other values (2)
77087 

Length

Max length9
Median length7
Mean length7.289393333
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowTuesday
3rd rowTuesday
4th rowTuesday
5th rowTuesday

Common Values

ValueCountFrequency (%)
Friday83548
18.6%
Thursday80468
17.9%
Wednesday80462
17.9%
Tuesday78965
17.5%
Saturday49470
11.0%
Sunday39254
8.7%
Monday37833
8.4%

Length

2022-02-16T16:53:29.647222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:29.714335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
friday83548
18.6%
thursday80468
17.9%
wednesday80462
17.9%
tuesday78965
17.5%
saturday49470
11.0%
sunday39254
8.7%
monday37833
8.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

order_time_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7238
Minimum0
Maximum21
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:29.799118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median14
Q316
95-th percentile17
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.619623125
Coefficient of variation (CV)0.2844765813
Kurtosis-0.9800672739
Mean12.7238
Median Absolute Deviation (MAD)2
Skewness-0.64855338
Sum5725710
Variance13.10167156
MonotonicityNot monotonic
2022-02-16T16:53:29.884851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1470587
15.7%
1666326
14.7%
1566184
14.7%
1753648
11.9%
1343407
9.6%
737651
8.4%
837553
8.3%
622886
 
5.1%
922245
 
4.9%
129262
 
2.1%
Other values (12)20251
 
4.5%
ValueCountFrequency (%)
02
 
< 0.1%
12
 
< 0.1%
261
 
< 0.1%
3645
 
0.1%
41211
 
0.3%
51089
 
0.2%
622886
5.1%
737651
8.4%
837553
8.3%
922245
4.9%
ValueCountFrequency (%)
212
 
< 0.1%
208
 
< 0.1%
1925
 
< 0.1%
18168
 
< 0.1%
1753648
11.9%
1666326
14.7%
1566184
14.7%
1470587
15.7%
1343407
9.6%
129262
 
2.1%

accept_time_hour
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct22
Distinct (%)< 0.1%
Missing157
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean12.7613434
Minimum0
Maximum21
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:29.971814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q19
median14
Q316
95-th percentile17
Maximum21
Range21
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.621517342
Coefficient of variation (CV)0.2837880956
Kurtosis-0.9784375606
Mean12.7613434
Median Absolute Deviation (MAD)2
Skewness-0.6478898375
Sum5740601
Variance13.11538786
MonotonicityNot monotonic
2022-02-16T16:53:30.051524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1469627
15.5%
1567104
14.9%
1666439
14.8%
1754529
12.1%
1341459
9.2%
737584
8.4%
837458
8.3%
923217
 
5.2%
621664
 
4.8%
129222
 
2.0%
Other values (12)21540
 
4.8%
ValueCountFrequency (%)
02
 
< 0.1%
12
 
< 0.1%
257
 
< 0.1%
3592
 
0.1%
41211
 
0.3%
51092
 
0.2%
621664
4.8%
737584
8.4%
837458
8.3%
923217
5.2%
ValueCountFrequency (%)
212
 
< 0.1%
208
 
< 0.1%
1931
 
< 0.1%
181422
 
0.3%
1754529
12.1%
1666439
14.8%
1567104
14.9%
1469627
15.5%
1341459
9.2%
129222
 
2.0%

first_order
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
1
449942 
0
 
58

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1449942
> 99.9%
058
 
< 0.1%

Length

2022-02-16T16:53:30.135345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:30.186629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
1449942
> 99.9%
058
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.4 MiB
0
449843 
1
 
157

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0449843
> 99.9%
1157
 
< 0.1%

Length

2022-02-16T16:53:30.241278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-02-16T16:53:30.293506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0449843
> 99.9%
1157
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

delivered_fraction
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1335
Distinct (%)0.3%
Missing16948
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean0.9901580054
Minimum0
Maximum1
Zeros393
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:30.361851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.9655172414
Q10.9887640449
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.01123595506

Descriptive statistics

Standard deviation0.03795187824
Coefficient of variation (CV)0.03832911316
Kurtosis450.6018829
Mean0.9901580054
Median Absolute Deviation (MAD)0
Skewness-18.87983676
Sum428789.9046
Variance0.001440345062
MonotonicityNot monotonic
2022-02-16T16:53:30.468648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1232686
51.7%
0.981184
 
0.3%
0.97777777781145
 
0.3%
0.98181818181139
 
0.3%
0.98214285711138
 
0.3%
0.98666666671136
 
0.3%
0.98876404491094
 
0.2%
0.97872340431094
 
0.2%
0.98751087
 
0.2%
0.9843751083
 
0.2%
Other values (1325)190266
42.3%
(Missing)16948
 
3.8%
ValueCountFrequency (%)
0393
0.1%
0.16666666672
 
< 0.1%
0.256
 
< 0.1%
0.333333333341
 
< 0.1%
0.410
 
< 0.1%
0.42857142861
 
< 0.1%
0.5224
< 0.1%
0.57142857141
 
< 0.1%
0.621
 
< 0.1%
0.6258
 
< 0.1%
ValueCountFrequency (%)
1232686
51.7%
0.99801587313
 
< 0.1%
0.9980039927
 
< 0.1%
0.997991967942
 
< 0.1%
0.99798387128
 
< 0.1%
0.997971602421
 
< 0.1%
0.99796747978
 
< 0.1%
0.997938144316
 
< 0.1%
0.997916666722
 
< 0.1%
0.997907949813
 
< 0.1%

accept_order_diff
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3243
Distinct (%)0.7%
Missing157
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean134.0647004
Minimum2
Maximum15598
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:30.576457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q133
median64
Q3112
95-th percentile517
Maximum15598
Range15596
Interquartile range (IQR)79

Descriptive statistics

Standard deviation283.955722
Coefficient of variation (CV)2.118049876
Kurtosis113.6159176
Mean134.0647004
Median Absolute Deviation (MAD)37
Skewness8.052822639
Sum60308067
Variance80630.85206
MonotonicityNot monotonic
2022-02-16T16:53:30.680727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85801
 
1.3%
95616
 
1.2%
105581
 
1.2%
75356
 
1.2%
115136
 
1.1%
124881
 
1.1%
134636
 
1.0%
144551
 
1.0%
154423
 
1.0%
64340
 
1.0%
Other values (3233)399522
88.8%
ValueCountFrequency (%)
21
 
< 0.1%
368
 
< 0.1%
4759
 
0.2%
52329
0.5%
64340
1.0%
75356
1.2%
85801
1.3%
95616
1.2%
105581
1.2%
115136
1.1%
ValueCountFrequency (%)
155981
< 0.1%
79051
< 0.1%
77261
< 0.1%
77081
< 0.1%
77041
< 0.1%
74521
< 0.1%
74421
< 0.1%
73031
< 0.1%
71781
< 0.1%
70391
< 0.1%

weekday_num
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.885242222
Minimum0
Maximum6
Zeros37833
Zeros (%)8.4%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:30.778668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.742035737
Coefficient of variation (CV)0.6037745196
Kurtosis-0.9533035557
Mean2.885242222
Median Absolute Deviation (MAD)1
Skewness0.116151224
Sum1298359
Variance3.034688507
MonotonicityNot monotonic
2022-02-16T16:53:30.847921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
483548
18.6%
380468
17.9%
280462
17.9%
178965
17.5%
549470
11.0%
639254
8.7%
037833
8.4%
ValueCountFrequency (%)
037833
8.4%
178965
17.5%
280462
17.9%
380468
17.9%
483548
18.6%
549470
11.0%
639254
8.7%
ValueCountFrequency (%)
639254
8.7%
549470
11.0%
483548
18.6%
380468
17.9%
280462
17.9%
178965
17.5%
037833
8.4%

call_total
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)< 0.1%
Missing159543
Missing (%)35.5%
Infinite0
Infinite (%)0.0%
Mean1.472162833
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:30.942204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum67
Range66
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.132203578
Coefficient of variation (CV)0.769074964
Kurtosis191.2203067
Mean1.472162833
Median Absolute Deviation (MAD)0
Skewness8.377347618
Sum427600
Variance1.281884942
MonotonicityNot monotonic
2022-02-16T16:53:31.038431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1207777
46.2%
256033
 
12.5%
315361
 
3.4%
45644
 
1.3%
52377
 
0.5%
61232
 
0.3%
7669
 
0.1%
8435
 
0.1%
9270
 
0.1%
10177
 
< 0.1%
Other values (35)482
 
0.1%
(Missing)159543
35.5%
ValueCountFrequency (%)
1207777
46.2%
256033
 
12.5%
315361
 
3.4%
45644
 
1.3%
52377
 
0.5%
61232
 
0.3%
7669
 
0.1%
8435
 
0.1%
9270
 
0.1%
10177
 
< 0.1%
ValueCountFrequency (%)
671
< 0.1%
571
< 0.1%
531
< 0.1%
481
< 0.1%
461
< 0.1%
451
< 0.1%
411
< 0.1%
391
< 0.1%
381
< 0.1%
371
< 0.1%

call_support
Real number (ℝ≥0)

MISSING

Distinct40
Distinct (%)0.2%
Missing431560
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean2.009815618
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:31.141147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum67
Range66
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.375750453
Coefficient of variation (CV)1.182073834
Kurtosis113.8322312
Mean2.009815618
Median Absolute Deviation (MAD)0
Skewness7.894350686
Sum37061
Variance5.644190215
MonotonicityNot monotonic
2022-02-16T16:53:31.235081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
110773
 
2.4%
23988
 
0.9%
31698
 
0.4%
4761
 
0.2%
5403
 
0.1%
6236
 
0.1%
7150
 
< 0.1%
8103
 
< 0.1%
963
 
< 0.1%
1046
 
< 0.1%
Other values (30)219
 
< 0.1%
(Missing)431560
95.9%
ValueCountFrequency (%)
110773
2.4%
23988
 
0.9%
31698
 
0.4%
4761
 
0.2%
5403
 
0.1%
6236
 
0.1%
7150
 
< 0.1%
8103
 
< 0.1%
963
 
< 0.1%
1046
 
< 0.1%
ValueCountFrequency (%)
671
< 0.1%
571
< 0.1%
521
< 0.1%
481
< 0.1%
461
< 0.1%
451
< 0.1%
411
< 0.1%
382
< 0.1%
371
< 0.1%
321
< 0.1%

call_customer
Real number (ℝ≥0)

MISSING

Distinct22
Distinct (%)< 0.1%
Missing71338
Missing (%)15.9%
Infinite0
Infinite (%)0.0%
Mean1.387947563
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 MiB
2022-02-16T16:53:31.328081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum24
Range23
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.8166234382
Coefficient of variation (CV)0.5883676445
Kurtosis34.18253652
Mean1.387947563
Median Absolute Deviation (MAD)0
Skewness4.003722539
Sum525563
Variance0.6668738399
MonotonicityNot monotonic
2022-02-16T16:53:31.412235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1277390
61.6%
273051
 
16.2%
318620
 
4.1%
45804
 
1.3%
52062
 
0.5%
6821
 
0.2%
7412
 
0.1%
8212
 
< 0.1%
9115
 
< 0.1%
1061
 
< 0.1%
Other values (12)114
 
< 0.1%
(Missing)71338
 
15.9%
ValueCountFrequency (%)
1277390
61.6%
273051
 
16.2%
318620
 
4.1%
45804
 
1.3%
52062
 
0.5%
6821
 
0.2%
7412
 
0.1%
8212
 
< 0.1%
9115
 
< 0.1%
1061
 
< 0.1%
ValueCountFrequency (%)
242
 
< 0.1%
212
 
< 0.1%
204
 
< 0.1%
195
 
< 0.1%
182
 
< 0.1%
171
 
< 0.1%
165
 
< 0.1%
154
 
< 0.1%
1412
< 0.1%
1315
< 0.1%

Interactions

2022-02-16T16:53:16.908056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:22.838280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:26.437748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:29.451993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:32.419072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:35.506695image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:38.602757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:41.796003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:44.975288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:49.477253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:52.755531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:55.953890image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:58.958332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:01.970951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:04.991176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.200303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:11.900635image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:14.761631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.065977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.017313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:26.606480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:29.632339image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:32.591430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:35.683675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:38.778413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:42.006194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:45.174315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:49.679029image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:52.938444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:56.166394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:59.129090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:02.145154image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:05.166976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.381971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:12.086391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:14.872989image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.219344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.181465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:26.782022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:29.794133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:32.757329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:35.856617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:38.943835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:42.198936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:45.370702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:49.864957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:53.105568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:56.328713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:59.292119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:02.312964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:05.340104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.549937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:12.270035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:14.981813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.376109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.341946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:26.945116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:29.962409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:32.956379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:36.027417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:39.112242image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:42.399436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:45.573150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:50.060942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:53.282270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:56.491167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:59.450915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:02.481287image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:05.510546image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.731027image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:12.436205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:15.099559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.529041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.512622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:27.108735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:30.124391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:33.126181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:36.199351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:39.279605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:42.588959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:45.780516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:50.273576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:53.456462image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:56.653752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:59.618394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:02.648329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:05.685459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.928238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:12.598861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:15.209082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.684968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.688976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:27.274865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:30.292654image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:33.298322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:36.374286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:39.446478image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:42.759594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:45.956608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:50.453144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:53.633186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:56.822760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:59.801853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:02.829217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:05.862763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:09.114625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:12.769524image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:15.324246image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:17.844098image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:23.854081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:27.443845image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2022-02-16T16:52:41.578434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:44.802299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:49.246388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:52.579777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:55.751608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:52:58.793147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:01.810620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:04.818867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:08.024675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:11.729077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:14.639402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2022-02-16T16:53:16.774127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2022-02-16T16:53:31.516792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-16T16:53:31.975572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-16T16:53:32.432474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-16T16:53:32.871520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-02-16T16:53:33.252925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-16T16:53:19.843091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-16T16:53:21.237354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-02-16T16:53:23.670507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-02-16T16:53:24.142208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

order_timeorder_idorder_dateallot_timeaccept_timepickup_timedelivered_timerider_idfirst_mile_distancelast_mile_distancealloted_ordersdelivered_orderscancelledundelivered_orderslifetime_order_countreassignment_methodreassignment_reasonreassigned_ordersession_timecancelled_timetotal_distweekdayorder_time_houraccept_time_hourfirst_ordercancel_before_acceptdelivered_fractionaccept_order_diffweekday_numcall_totalcall_supportcall_customer
02021-01-26 02:21:355567532021-01-262021-01-26 02:21:592021-01-26 02:22:082021-01-26 02:32:512021-01-26 02:49:47116961.5666002.6546.046.000.0621.0NaNNaN0.0175.550000NaT4.216600Tuesday22.0101.00000033.012.0NaN1.0
12021-01-26 02:33:165567542021-01-262021-01-26 02:33:572021-01-26 02:34:452021-01-26 02:50:252021-01-26 03:11:15181172.5207002.768.08.000.0105.0NaNNaN0.03.266667NaT5.280700Tuesday22.0101.00000089.011.01.0NaN
22021-01-26 02:39:495567552021-01-262021-01-26 02:39:572021-01-26 02:40:132021-01-26 02:56:002021-01-26 03:12:46186232.2074004.801.01.000.066.0NaNNaN0.09.816667NaT7.007400Tuesday22.0101.00000024.01NaNNaN1.0
32021-01-26 02:47:535567562021-01-262021-01-26 02:48:252021-01-26 02:49:062021-01-26 03:21:512021-01-26 03:41:05159452.1894006.381.01.000.0127.0NaNNaN0.017.533333NaT8.569400Tuesday22.0101.00000073.013.02.0NaN
42021-01-26 03:06:305567572021-01-262021-01-26 03:07:212021-01-26 03:07:572021-01-26 03:31:382021-01-26 04:00:15175892.7870004.0134.034.000.084.0NaNNaN0.01.350000NaT6.797000Tuesday33.0101.00000087.011.0NaN1.0
52021-01-26 03:07:165567582021-01-262021-01-26 03:12:142021-01-26 03:12:272021-01-26 03:25:362021-01-26 03:45:5114692.4818005.18296.0294.002.01506.0NaNNaN0.0175.550000NaT7.661800Tuesday33.0100.993243311.011.0NaN1.0
62021-01-26 03:10:505567592021-01-262021-01-26 03:11:182021-01-26 03:12:052021-01-26 03:19:312021-01-26 03:26:0488512.8091003.4045.045.000.01460.0NaNNaN0.0175.550000NaT6.209100Tuesday33.0101.00000075.012.0NaN1.0
72021-01-26 03:14:105567602021-01-262021-01-26 03:14:382021-01-26 03:14:442021-01-26 03:33:532021-01-26 03:42:3884930.0256810.1654.053.001.0270.0NaNNaN0.044.166667NaT0.185681Tuesday33.0100.98148134.01NaNNaN2.0
82021-01-26 03:14:205567612021-01-262021-01-26 03:14:502021-01-26 03:15:142021-01-26 04:00:022021-01-26 04:13:31115432.4442002.8629.029.000.0955.0NaNNaN0.02.500000NaT5.304200Tuesday33.0101.00000054.01NaNNaN1.0
92021-01-26 03:15:185567622021-01-262021-01-26 03:21:272021-01-26 03:22:042021-01-26 04:14:562021-01-26 04:38:39210372.8786002.610.00.000.01.0autoReassignment Request from SE portal.1.0175.550000NaT5.488600Tuesday33.010NaN406.01NaNNaN1.0

Last rows

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4499902021-02-06 10:03:001302212021-02-062021-02-06 10:03:012021-02-06 10:04:032021-02-06 10:07:442021-02-06 10:15:0336172.19651.2422.022.000.034.0NaNNaN0.0376.416667NaT3.4365Saturday1010.0101.00000063.05NaNNaNNaN
4499912021-02-06 10:03:011302222021-02-062021-02-06 10:04:302021-02-06 10:06:042021-02-06 10:26:232021-02-06 10:36:097731.47071.12147.0146.001.0773.0NaNNaN0.0268.166667NaT2.5907Saturday1010.0100.993197183.051.0NaNNaN
4499922021-02-06 10:03:061302232021-02-062021-02-06 10:03:472021-02-06 10:04:072021-02-06 10:15:012021-02-06 10:26:0614070.30071.26265.0264.001.0477.0NaNNaN0.0209.266667NaT1.5607Saturday1010.0100.99622661.051.0NaNNaN
4499932021-02-06 10:03:111302242021-02-062021-02-06 10:03:122021-02-06 10:03:352021-02-06 10:15:432021-02-06 10:25:147452.70133.72200.0200.000.0290.0NaNNaN0.0229.250000NaT6.4213Saturday1010.0101.00000024.051.0NaNNaN
4499942021-02-06 10:03:141302252021-02-062021-02-06 10:03:232021-02-06 10:03:432021-02-06 10:15:362021-02-06 10:34:1078231.79162.60202.0201.001.01426.0NaNNaN0.0273.233333NaT4.3916Saturday1010.0100.99505029.05NaNNaNNaN
4499952021-02-06 10:03:161302262021-02-062021-02-06 10:03:442021-02-06 10:04:142021-02-06 10:27:292021-02-06 10:44:0810060.57890.194.04.000.0127.0NaNNaN0.0369.516667NaT0.7689Saturday1010.0101.00000058.051.0NaNNaN
4499962021-02-06 10:03:171302272021-02-062021-02-06 10:03:182021-02-06 10:04:342021-02-06 10:22:172021-02-06 10:31:432791.98631.1981.081.000.0105.0NaNNaN0.0239.133333NaT3.1763Saturday1010.0101.00000077.051.0NaNNaN
4499972021-02-06 10:03:181302282021-02-062021-02-06 10:04:062021-02-06 10:04:392021-02-06 10:19:062021-02-06 10:26:5631611.59441.6128.028.000.01488.0NaNNaN0.0204.150000NaT3.2044Saturday1010.0101.00000081.052.0NaNNaN
4499982021-02-06 10:03:191302292021-02-062021-02-06 10:03:192021-02-06 10:05:412021-02-06 10:20:392021-02-06 10:30:4193962.89394.6872.072.000.0105.0NaNNaN0.065.583333NaT7.5739Saturday1010.0101.000000142.054.0NaNNaN
4499992021-02-06 10:03:241302302021-02-062021-02-06 10:03:452021-02-06 10:05:142021-02-06 10:13:262021-02-06 10:19:4120781.89250.0930.030.000.0108.0NaNNaN0.0212.000000NaT1.9825Saturday1010.0101.000000110.051.0NaNNaN